Description Usage Arguments Value References Examples

`crseEvent`

implements a robust statistical test developed by Dutta et al. (JempFin, 2018).

The test is based on abnormal standardized returns and offers three implementations. Standardized returns are defined as *sr_{it} = \frac{r_{it}}{s_{it}}* where *s_{it}* is a standard deviation estimator of log returns *r_{it}*:

**Use of Abnormal standardized returns (ASR)**

Abnormal standardized returns are defined as *ASR_{it} = sr_{it} - sr_{ci,t}*, where *sr_{ci,t}* is the standardized return of the matching control firm or the average of standardized returns of the matching control portfolio.

**Use of Standardized abnormal returns (SAR)**

Standardized abnormal returns are defined as *SAR_{it} = \frac{r_{event} - r_{control}}{sd_{event-control}}*. The matching control return should be derived from a single firm observation and not be the return-series of a portfolio.

**Use of Continuously compounded abnormal returns (CCAR)**

Continuously compounded abnormal returns are defined as *CCAR_{it} = r_{it} - r_{ci,t}*, where *r_{it} = log(1 + R_{it})* is the event month *t* continuously compounded return (i.e., log-return) of event stock *i*, and *r_{ci,t}* is the continuously compounded return of the control firm.

1 2 |

`data` |
an object of class |

`abnr` |
Name of a column from |

`cluster1` |
Name of a column from |

`cluster2` |
Name of a column from |

`na.rm` |
An object of class |

`na.replace` |
A numeric scalar: If |

`crseEvent`

returns an object of `class`

`crse`

and `list`

.

The returning value of `"crseEvent"`

is a `"list"`

containing the
following components:

`N` |
Total number of observations. |

`mean.abnormal.ret` |
Mean abnormal return. |

`t.val.nonclustered` |
Non-clustered (common) t-value. |

`p.val.nonclustered` |
Non-clustered (common) p-value. |

`t.val.one.clustered` |
One-way clustered t-value. |

`p.val.one.clustered` |
One-way clustered p-value. |

`tcl2` |
One-way clustering t-value with respect to second clustering variable ( |

`pcl2` |
One-way clustering p-value with respect to second clustering variable ( |

`tcl12` |
2-way clustering t-value ( |

`pcl12` |
2-way clustering p-value ( |

`cluster1` |
Name of the first cluster variable. |

`cluster2` |
Name of the second cluster variable. |

`reg.fit` |
Regression results on which t-value compuations are based. |

`var.cl1` |
Robust variance of abnormal return series with regard to one-way clustering on variable |

`var.cl2` |
Robust variance of abnormal return series with regard to one-way clustering on variable |

`var.cl12` |
Robust variance of abnormal return series with regard to two-way clustering on both variable |

`unique.cl1` |
Total number of unique observations by clustering on variable |

`unique.cl2` |
Total number of unique observations by clustering on variable |

Dutta, A., Knif, J., Kolari, J.W., Pynnonen, S. (2018):
A robust and powerful test of abnormal stock returns in long-horizon event studies.
*Journal of Empirical Finance*, **47**, p. 1-24.
doi: 10.1016/j.jempfin.2018.02.004.

1 2 3 4 5 6 7 8 | ```
## load demo_share_repurchases
## one-way clustering on column "date" and print summary statistics
data(demo_share_repurchases)
crse <- crseEvent(demo_share_repurchases, abnr="ars", cluster1 = "date")
summary(crse)
## print mean of abnormal return series
crse$mean.abnormal.ret
``` |

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